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# ==========================================================================

# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================

# Copyright (c) OpenMMLab. All rights reserved.
import os
import random
import warnings

import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
                         build_runner, get_dist_info)
from mmcv.utils import build_from_cfg

from mmseg import digit_version
from mmseg.core import DistEvalHook, EvalHook, build_optimizer
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.utils import (build_ddp, build_dp, find_latest_checkpoint,
                         get_root_logger)

from apex import amp

def init_random_seed(seed=None, device='npu'):
    """Initialize random seed.

    If the seed is not set, the seed will be automatically randomized,
    and then broadcast to all processes to prevent some potential bugs.
    Args:
        seed (int, Optional): The seed. Default to None.
        device (str): The device where the seed will be put on.
            Default to 'cuda'.
    Returns:
        int: Seed to be used.
    """
    if seed is not None:
        return seed

    # Make sure all ranks share the same random seed to prevent
    # some potential bugs. Please refer to
    # https://github.com/open-mmlab/mmdetection/issues/6339
    rank, world_size = get_dist_info()
    seed = np.random.randint(2**31)
    if world_size == 1:
        return seed

    if rank == 0:
        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
    else:
        random_num = torch.tensor(0, dtype=torch.int32, device=device)
    dist.broadcast(random_num, src=0)
    return random_num.item()


def set_random_seed(seed, deterministic=False):
    """Set random seed.

    Args:
        seed (int): Seed to be used.
        deterministic (bool): Whether to set the deterministic option for
            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
            to True and `torch.backends.cudnn.benchmark` to False.
            Default: False.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.npu.manual_seed_all(seed)
    if deterministic:
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False


def train_segmentor(model,
                    dataset,
                    cfg,
                    distributed=False,
                    validate=False,
                    timestamp=None,
                    meta=None):
    """Launch segmentor training."""
    logger = get_root_logger(cfg.log_level)

    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    # The default loader config
    loader_cfg = dict(
        # cfg.gpus will be ignored if distributed
        num_gpus=len(cfg.gpu_ids),
        dist=distributed,
        seed=cfg.seed,
        drop_last=True)
    # The overall dataloader settings
    loader_cfg.update({
        k: v
        for k, v in cfg.data.items() if k not in [
            'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
            'test_dataloader'
        ]
    })

    # The specific dataloader settings
    train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]

     # build runner
    optimizer = build_optimizer(model, cfg.optimizer)
    model,optimizer = amp.initialize(model.npu(),
                                    optimizer,
                                    opt_level='O1',
                                    loss_scale='dynamic',
                                    combine_grad=True
                                    )
    
    # put model on devices
    if distributed:
        find_unused_parameters = cfg.get('find_unused_parameters', False)
        # Sets the `find_unused_parameters` parameter in
        # DDP wrapper
        model = build_ddp(
            model,
            cfg.device,
            device_ids=[int(os.environ['LOCAL_RANK'])],
            broadcast_buffers=False,
            find_unused_parameters=find_unused_parameters)
    else:
        if not torch.npu.is_available():
            assert digit_version(mmcv.__version__) >= digit_version('1.4.4'), \
                'Please use MMCV >= 1.4.4 for CPU training!'
        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)


    if cfg.get('runner') is None:
        cfg.runner = {'type': 'IterBasedRunner', 'max_iters': cfg.total_iters}
        warnings.warn(
            'config is now expected to have a `runner` section, '
            'please set `runner` in your config.', UserWarning)

    runner = build_runner(
        cfg.runner,
        default_args=dict(
            model=model,
            batch_processor=None,
            optimizer=optimizer,
            work_dir=cfg.work_dir,
            logger=logger,
            meta=meta))

    # register hooks
    runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
                                   cfg.checkpoint_config, cfg.log_config,
                                   cfg.get('momentum_config', None))
    if distributed:
        # when distributed training by epoch, using`DistSamplerSeedHook` to set
        # the different seed to distributed sampler for each epoch, it will
        # shuffle dataset at each epoch and avoid overfitting.
        if isinstance(runner, EpochBasedRunner):
            runner.register_hook(DistSamplerSeedHook())

    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp

    # register eval hooks
    if validate:
        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
        # The specific dataloader settings
        val_loader_cfg = {
            **loader_cfg,
            'samples_per_gpu': 1,
            'shuffle': False,  # Not shuffle by default
            **cfg.data.get('val_dataloader', {}),
        }
        val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
        eval_cfg = cfg.get('evaluation', {})
        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
        eval_hook = DistEvalHook if distributed else EvalHook
        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
        runner.register_hook(
            eval_hook(val_dataloader, **eval_cfg), priority='LOW')

    # user-defined hooks
    if cfg.get('custom_hooks', None):
        custom_hooks = cfg.custom_hooks
        assert isinstance(custom_hooks, list), \
            f'custom_hooks expect list type, but got {type(custom_hooks)}'
        for hook_cfg in cfg.custom_hooks:
            assert isinstance(hook_cfg, dict), \
                'Each item in custom_hooks expects dict type, but got ' \
                f'{type(hook_cfg)}'
            hook_cfg = hook_cfg.copy()
            priority = hook_cfg.pop('priority', 'NORMAL')
            hook = build_from_cfg(hook_cfg, HOOKS)
            runner.register_hook(hook, priority=priority)

    if cfg.resume_from is None and cfg.get('auto_resume'):
        resume_from = find_latest_checkpoint(cfg.work_dir)
        if resume_from is not None:
            cfg.resume_from = resume_from
    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow)
